7,600 research outputs found
End-to-end Driving via Conditional Imitation Learning
Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. This limits the utility of such systems. We propose to
condition imitation learning on high-level command input. At test time, the
learned driving policy functions as a chauffeur that handles sensorimotor
coordination but continues to respond to navigational commands. We evaluate
different architectures for conditional imitation learning in vision-based
driving. We conduct experiments in realistic three-dimensional simulations of
urban driving and on a 1/5 scale robotic truck that is trained to drive in a
residential area. Both systems drive based on visual input yet remain
responsive to high-level navigational commands. The supplementary video can be
viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation
(ICRA), 201
New Proposed Mechanism of Actin-Polymerization-Driven Motility
We present the first numerical simulation of actin-driven propulsion by
elastic filaments. Specifically, we use a Brownian dynamics formulation of the
dendritic nucleation model of actin-driven propulsion. We show that the model
leads to a self-assembled network that exerts forces on a disk and pushes it
with an average speed. This simulation approach is the first to observe a speed
that varies non-monotonically with the concentration of branching proteins
(Arp2/3), capping protein and depolymerization rate (ADF), in accord with
experimental observations. Our results suggest a new interpretation of the
origin of motility that can be tested readily by experiment.Comment: 31 pages, 5 figure
Role of electrostatic forces in cluster formation in a dry ionomer
This simulation study investigates the dependence of the structure of dry
Nafion-like ionomers on the electrostatic interactions
between the components of the molecules. In order to speed equilibration, a
procedure was adopted which involved detaching the side chains from the
backbone and cutting the backbone into segments, and then reassembling the
macromolecule by means of a strong imposed attractive force between the cut
ends of the backbone, and between the non-ionic ends of the side chains and the
midpoints of the backbone segments. Parameters varied in this study include the
dielectric constant, the free volume, side-chain length, and strength of
head-group interactions. A series of coarse-grained mesoscale simulations shows
the morphlogy to depend sensitively on the ratio of the strength of the
dipole-dipole interactions between the side-chain acidic end groups to the
strength of the other electrostatic components of the Hamiltonian. Examples of
the two differing morphologies proposed by Gierke and by Gebel emerge from our
simulations.Comment: 39 pages, 18 figures, accepted for publicatio
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin
On Offline Evaluation of Vision-based Driving Models
Autonomous driving models should ideally be evaluated by deploying them on a
fleet of physical vehicles in the real world. Unfortunately, this approach is
not practical for the vast majority of researchers. An attractive alternative
is to evaluate models offline, on a pre-collected validation dataset with
ground truth annotation. In this paper, we investigate the relation between
various online and offline metrics for evaluation of autonomous driving models.
We find that offline prediction error is not necessarily correlated with
driving quality, and two models with identical prediction error can differ
dramatically in their driving performance. We show that the correlation of
offline evaluation with driving quality can be significantly improved by
selecting an appropriate validation dataset and suitable offline metrics. The
supplementary video can be viewed at
https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc
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